Machine learning for industrial safety culture in the developing world

Abstract

A disproportionate number of deaths and injuries in the workplace are concentrated in the developing world due to inadequate enforcement capabilities in such areas, a lack of capital equipment and a paucity of expertise. This requires solutions that enable automated detection of harmful violations of safety standards and sending appropriate warnings at real-time. We propose an automated method for highlighting such violations of safety measures by foregoing the usage of safety helmets, hard hats and similar safety equipment in the factory setting. A pipeline is proposed wherein camera feed at real-time is processed to detect the safety critical objects and their placement using a set of deep learning based architectures tailored for detection and localization under compute-constrained environments, with the helmet compliance sub-task treated as an exemplar of our approach.

Tasks

Reproductions